Gliederung

Introduction

In medicine many exceptions occur. In medical practice and in knowledge-based systems, these exceptions have to be considered and have to be dealt with appropriately. In medical studies and in research, exceptions shall be explained. We have developed ISOR, a case-based dialogue system that helps doctors to explain exceptional cases. ISOR deals with situations where neither a well-developed theory nor reliable knowledge nor a proper case base is available. So, just some theoretical hypothesis and a set of measurements are given. In such situations the usual question is, how do measured data fit to theoretical hypotheses. To statistically confirm a hypothesis it is necessary that the majority of cases fit the hypothesis. Statistics determines the exact quantity of necessary confirmation. However, usually a few cases do not fit the hypothesis. We examine these cases to find out why they do not satisfy the hypothesis. ISOR offers a dialogue to guide the search for possible reasons in all components of the data system. The exceptional cases belong to the case base. This approach is justified by a certain mistrust of statistical models by doctors, because modelling results are usually unspecific and ”average oriented”, which means a lack of attention to individual "imperceptible" features of concrete patients.

The usual case-based reasoning (CBR) assumption is that a case base with complete solutions is available. Our approach starts in a situation where such a case base is not available but has to be set up incrementally. So, we must construct a model, point out the exceptions, find causes why the exceptional cases do not fit the model, and set up a case base.

Material and Methods

Hemodialysis means stress for a patient’s organism and has significant adverse effects. Fitness is the most available and a relative cheap way of support. At the University clinic in St. Petersburg, a specially developed complex of physiotherapy exercises including simulators, walking, swimming and so on. was offered to all dialysis patients but only some of them actively participated, whereas some others participated but were not really active. For each patient a set of physiological parameters was measured. More than 100 parameters were planned for every patient. The parameters were supposed to be measured four times during the first year of participating in the fitness program. There was an initial measurement followed by a next one after three months, then after six months and finally after a year. Unfortunately, since some measurements did not happen, many data are missing. Non-homogeneity of observed data, many missing values, many parameters for a relatively small sample size, all this makes our data set practically impossible for usual statistical analysis.

According to our assumption, active patients should feel better after some months of fitness, whereas non-active ones should feel rather worse. We have to define the meaning of ”feeling better” and ”feeling worse” in our context. Therefor, a medical expert selects these factors from ISOR’s menu: oxygen pulse by training, maximal uptake of oxygen by training, and performed work (joules) during control training.

Subsequently the ”research time period” has to be determined. Initially, this period was planned to be twelve months, but after a while the patients tend to give up the fitness program. This means, the longer the time period, the more data are missing. Therefore, we have to make a compromise between time period and sample size. A period of six months is chosen.

The next question is whether the model shall be quantitative or qualitative? We compare every patient with his own situation some months ago, namely just before the start of the fitness program. The success shall not be measured in absolute values, because the health statuses of patients are very different. Thus, even a modest improvement for one patient may be as important as a great improvement of another. Therefore, we simply classify the development in two categories: ”better” and ”worse”.

The three main factors are supposed to describe the changes of the physical conditions of the patients. The changes are assessed depending on the number of improved factors:

Weak version of the model: at least one factor has improved

Medium version of the model: at least two factors have improved

Strong version of the model: all three factors have improved

The final step means to define the type of model. Popular statistical programs offer a large variety of statistical models. Some of them deal with categorical data. The easiest model is a 2x2 frequency table. Our ”Better/ Worse” concept fits this simple model very well. So the 2x2 frequency table is accepted. The results are presented in Table 1 [Tab.Â 1].

Statistical analysis shows a significant dependence between the patients activity and improvement of their physical condition. Unfortunately, the most popular Pearson Chi-square test is not applicable here because of the small values ”2” and ”3” in table 1. But Fisher’s exact test can be used. In the three versions shown in table 1 a very strong significance can be observed. The smaller the value of p is, the more significant the dependency.

Though the performed Fisher test confirms the hypothesis, there are exceptions, namely active patients whose health conditions did not improve. Exceptions should be explained. Already explained exceptions build the case base and can be considered for explaining further exceptional cases.

Results

As results we obtain a set of solutions of different origin and different nature. There are three categories of solution: additional factor, model failure, and wrong data.

The most important and most frequent solution is the influence of an additional factor. Only three main factors are obviously not enough to describe all medical cases. Unfortunately, for different patients different additional factors are important. When ISOR has discovered an additional factor as explanation for an exceptional case, the factor has to be confirmed by a medical expert before it can be accepted as a solution. One of these factors is Parathyroid Hormone (PTH). An increased PTH level sometimes can explain a worsened condition of a patient. PTH is a significant factor, but unfortunately it was measured only for some patients. Another additional factor as an explanation is phosphorus blood level.

We regard two types of model failures. One of them is deliberately neglected data. As a compromise we just consider data of six months, whereas further data of a patient might be important. In fact, three of the patients did not show an improvement in the considered six month but in the following six months. So, they were wrongly classified and should really belong to the ”better” category. The second type of model failure is based on the fact that the two-category model is not precise enough. Some exceptions can be explained by a tiny and not really significant change in one of the main factors.

Wrong data are usually due to a technical mistake or to not really proved data. One patient, for example, was reported as actively participating in the fitness program but really was not.

Discussion

We propose to use CBR to explain cases that do not fit a statistical model. Here we presented one of the simplest models. However, it is relatively effective, because it demonstrates statistically significant dependencies, in our example between fitness activity and health improvement of dialysis patients, where the model covers about two thirds of the patients, whereas the other third can be explained by applying CBR. The presented method makes use of different sources of knowledge and information, including medical experts. It seems to be a very promising method to deal with a poorly structured database, with many missing data, and with situations where cases contain different sets of attributes.